xgboost::linalg Namespace Reference

Namespaces
	detail

Classes
class	TensorView
	A tensor view with static type and dimension. It implements indexing and slicing. More...
class	Tensor
	A tensor storage. To use it for other functionality like slicing one needs to obtain a view first. This way we can use it on both host and device. More...

Typedefs
template<typename T >
using	VectorView = TensorView< T, 1 >
	A view over a vector, specialization of Tensor. More...
template<typename T >
using	MatrixView = TensorView< T, 2 >
	A view over a matrix, specialization of Tensor. More...
template<typename T >
using	Matrix = Tensor< T, 2 >
template<typename T >
using	Vector = Tensor< T, 1 >

Enumerations
enum	Order : std::uint8_t { kC , kF }

Functions
constexpr detail::AllTag	All ()
	Specify all elements in the axis for slicing. More...
template<typename I >
constexpr detail::RangeTag< I >	Range (I beg, I end)
	Specify a range of elements in the axis for slicing. More...
template<typename Container , typename... S, std::enable_if_t<!common::detail::IsSpan< Container >::value &&!std::is_pointer_v< Container >> * = nullptr>
auto	MakeTensorView (Context const *ctx, Container &data, S &&...shape)
	Constructor for automatic type deduction. More...
template<typename T , typename... S>
LINALG_HD auto	MakeTensorView (std::int32_t device, common::Span< T > data, S &&...shape)
template<typename T , typename... S>
auto	MakeTensorView (Context const *ctx, common::Span< T > data, S &&...shape)
template<typename T , typename... S>
auto	MakeTensorView (Context const *ctx, HostDeviceVector< T > *data, S &&...shape)
template<typename T , typename... S>
auto	MakeTensorView (Context const *ctx, HostDeviceVector< T > const *data, S &&...shape)
template<size_t D>
LINALG_HD auto	UnravelIndex (size_t idx, common::Span< size_t const, D > shape)
	Turns linear index into multi-dimension index. Similar to numpy unravel. More...
template<size_t D>
LINALG_HD auto	UnravelIndex (size_t idx, std::size_t const (&shape)[D])
template<typename... S>
LINALG_HD auto	UnravelIndex (std::size_t idx, S... shape)
template<typename T >
auto	MakeVec (T *ptr, size_t s, int32_t device=-1)
	Create a vector view from contigious memory. More...
template<typename T >
auto	MakeVec (HostDeviceVector< T > *data)
template<typename T >
auto	MakeVec (HostDeviceVector< T > const *data)
template<typename T , int32_t D>
Json	ArrayInterface (TensorView< T const, D > const &t)
	Array Interface defined by numpy. More...
template<typename T , int32_t D>
Json	ArrayInterface (TensorView< T, D > const &t)
	Same as const version, but returns non-readonly data pointer. More...
template<typename T , int32_t D>
auto	ArrayInterfaceStr (TensorView< T const, D > const &t)
	Return string representation of array interface. More...
template<typename T , int32_t D>
auto	ArrayInterfaceStr (TensorView< T, D > const &t)
template<typename T , typename... Index>
auto	Empty (Context const *ctx, Index &&...index)
	Create an array without initialization. More...
template<typename T , typename... Index>
auto	Constant (Context const *ctx, T v, Index &&...index)
	Create an array with value v. More...
template<typename T , typename... Index>
auto	Zeros (Context const *ctx, Index &&...index)
	Like np.zeros, return a new array of given shape and type, filled with zeros. More...
template<typename T , int32_t D>
void	Stack (Tensor< T, D > *l, Tensor< T, D > const &r)

Typedef Documentation

Matrix

template<typename T >

using xgboost::linalg::Matrix = typedef Tensor<T, 2>

Examples

inference.c.

MatrixView

template<typename T >

using xgboost::linalg::MatrixView = typedef TensorView<T, 2>

A view over a matrix, specialization of Tensor.

Template Parameters

T	data type of matrix

Vector

template<typename T >

using xgboost::linalg::Vector = typedef Tensor<T, 1>

VectorView

template<typename T >

using xgboost::linalg::VectorView = typedef TensorView<T, 1>

A view over a vector, specialization of Tensor.

Template Parameters

T	data type of vector

Enumeration Type Documentation

Order

enum xgboost::linalg::Order : std::uint8_t

Enumerator
kC
kF

Function Documentation

All()

constexpr detail::AllTag xgboost::linalg::All ( )

constexpr

Specify all elements in the axis for slicing.

ArrayInterface() [1/2]

template<typename T , int32_t D>

Json xgboost::linalg::ArrayInterface

(

TensorView< T const, D > const &

t

)

Array Interface defined by numpy.

stream is optionally included when data is on CUDA device.

ArrayInterface() [2/2]

template<typename T , int32_t D>

Json xgboost::linalg::ArrayInterface

(

TensorView< T, D > const &

t

)

Same as const version, but returns non-readonly data pointer.

ArrayInterfaceStr() [1/2]

template<typename T , int32_t D>

auto xgboost::linalg::ArrayInterfaceStr

(

TensorView< T const, D > const &

t

)

Return string representation of array interface.

ArrayInterfaceStr() [2/2]

template<typename T , int32_t D>

auto xgboost::linalg::ArrayInterfaceStr

(

TensorView< T, D > const &

t

)

Constant()

template<typename T , typename... Index>

auto xgboost::linalg::Constant	(	Context const *	ctx,
		T	v,
		Index &&...	index
	)

Create an array with value v.

Empty()

template<typename T , typename... Index>

auto xgboost::linalg::Empty	(	Context const *	ctx,
		Index &&...	index
	)

Create an array without initialization.

MakeTensorView() [1/5]

template<typename T , typename... S>

auto xgboost::linalg::MakeTensorView	(	Context const *	ctx,
		common::Span< T >	data,
		S &&...	shape
	)

MakeTensorView() [2/5]

template<typename Container , typename... S, std::enable_if_t<!common::detail::IsSpan< Container >::value &&!std::is_pointer_v< Container >> * = nullptr>

auto xgboost::linalg::MakeTensorView	(	Context const *	ctx,
		Container &	data,
		S &&...	shape
	)

Constructor for automatic type deduction.

MakeTensorView() [3/5]

template<typename T , typename... S>

auto xgboost::linalg::MakeTensorView	(	Context const *	ctx,
		HostDeviceVector< T > *	data,
		S &&...	shape
	)

MakeTensorView() [4/5]

template<typename T , typename... S>

auto xgboost::linalg::MakeTensorView	(	Context const *	ctx,
		HostDeviceVector< T > const *	data,
		S &&...	shape
	)

MakeTensorView() [5/5]

template<typename T , typename... S>

LINALG_HD auto xgboost::linalg::MakeTensorView	(	std::int32_t	device,
		common::Span< T >	data,
		S &&...	shape
	)

MakeVec() [1/3]

template<typename T >

auto xgboost::linalg::MakeVec

(

HostDeviceVector< T > *

data

)

MakeVec() [2/3]

template<typename T >

auto xgboost::linalg::MakeVec

(

HostDeviceVector< T > const *

data

)

MakeVec() [3/3]

template<typename T >

auto xgboost::linalg::MakeVec	(	T *	ptr,
		size_t	s,
		int32_t	device = -1
	)

Create a vector view from contigious memory.

Parameters

ptr	Pointer to the contigious memory.
s	Size of the vector.
device	(optional) Device ordinal, default to be host.

Range()

template<typename I >

constexpr detail::RangeTag<I> xgboost::linalg::Range ( I  beg, I  end  )

constexpr

Specify a range of elements in the axis for slicing.

Stack()

template<typename T , int32_t D>

void xgboost::linalg::Stack	(	Tensor< T, D > *	l,
		Tensor< T, D > const &	r
	)

UnravelIndex() [1/3]

template<size_t D>

LINALG_HD auto xgboost::linalg::UnravelIndex	(	size_t	idx,
		common::Span< size_t const, D >	shape
	)

Turns linear index into multi-dimension index. Similar to numpy unravel.

UnravelIndex() [2/3]

template<size_t D>

LINALG_HD auto xgboost::linalg::UnravelIndex	(	size_t	idx,
		std::size_t const (&)	shape[D]
	)

UnravelIndex() [3/3]

template<typename... S>

LINALG_HD auto xgboost::linalg::UnravelIndex	(	std::size_t	idx,
		S...	shape
	)

Zeros()

template<typename T , typename... Index>

auto xgboost::linalg::Zeros	(	Context const *	ctx,
		Index &&...	index
	)

Like np.zeros, return a new array of given shape and type, filled with zeros.